Financial forecastings using neural networks ppt

The aim of the project is to predict the interest rates,bond yield variation and stock market prices using neural networks and make a comparative study of different pre-processing techniques viz Fast Fourier Transform and Hilbert Huang Transform.this ppt needs other two also..

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NEURAL NETWORKS <br />Map some type of input stream of information to an output stream of data. <br />They derive non-linear modelsthat can be trained to map past and future values of the input output relationship .It extracts relationships governing the data that was not obvious using other analytical tools. <br />Capability to recognize patternand the speed of techniques to accurately solve complex processes, exploited exhaustively in financial forecasting.<br />Trained without the restriction of a model to derive parameters and discover relationships, driven and shaped solely by the nature of the data. <br />

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NEURAL NETWORKS V/S CONVENTIONAL COMPUTERS<br />Neural networks have the unique capability of learning thus are adaptive .This problem solving tool creates a unique likeness to the human brain .<br />Use the interconnectedness of the elements of the model rather than follow a set of sequential steps, that may or may not solve the problem like computers do.<br />A different aspect of model building, where the unique relationships between the variables creates the model, rather than trying to force variables to conform to a theoretical abstract that may or may not exist.<br />

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NEURAL NETWORKS IN FINANCE<br />Neural networks are trained without the restriction of a model to derive parameters and discover relationships, driven and shaped solely by the nature of the data. Thus it has profound implications and applicability to the finance field.<br />Some of the fields where it is applied are: <br /> Financial forecasting<br /> Capital budgeting and risk management <br /> Stock market analysis<br />Used to analyze and verify Economic hypothesis and theories which were not possible otherwise. <br />Govt. predicts interest rates to gauge the future inflationary situation of its economy .<br />

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Neural Networking and Similarities with the Workings of the Human Brain<br />

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BACK-PROPOGATION<br />Numerous such input/target pairs are used to train the Neural Network.<br />

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TIME SERIES FORECASTING<br />Time series forecastingor time series prediction, takes an existing series of data and forecasts the data values. The goal is to observe or model the existing data series to enable future unknown data values to be forecasted accurately.<br />Done using the NARX model or NAR model .<br />

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DIFFICULTIES<br />Limited quantity of data .<br />Noise in data – It obscures the underlying pattern of the data .<br />Non-stationarity - data that do not have the same statistical properties (e.g., mean and variance) at each point in time<br />Appropriate Forecasting Technique Selection .<br />